// Cloned by akshara on 5 Dec 2020 from World "Character recognition neural network" by "Coding Train" project
// Please leave this clone trail here.
// Port of Character recognition neural network from here:
// https://github.com/CodingTrain/Toy-Neural-Network-JS/tree/master/examples/mnist
// with many modifications
// --- defined by MNIST - do not change these ---------------------------------------
AB.runloggedin; // Boolean. Are we running logged in.
AB.myuserid;
const PIXELS = 28; // images in data set are tiny
const PIXELSSQUARED = PIXELS * PIXELS;
// number of training and test exemplars in the data set:
const NOTRAIN = 60000;
const NOTEST = 10000;
//--- can modify all these --------------------------------------------------
// no of nodes in network
const noinput = PIXELSSQUARED;
const nohidden = 128;
const nooutput = 10;
const learningrate = 0.1; // default 0.1
// should we train every timestep or not
let do_training = true;
// how many to train and test per timestep
const TRAINPERSTEP = 30;
const TESTPERSTEP = 5;
// multiply it by this to magnify for display
const ZOOMFACTOR = 8;
const ZOOMPIXELS = ZOOMFACTOR * PIXELS;
// 3 rows of
// large image + 50 gap + small image
// 50 gap between rows
const canvaswidth = ( PIXELS + ZOOMPIXELS ) + 50;
const canvasheight = ( ZOOMPIXELS * 3 ) + 100;
const DOODLE_THICK = 24; // thickness of doodle lines
const DOODLE_BLUR = 7; // blur factor applied to doodles
let mnist;
// all data is loaded into this
// mnist.train_images
// mnist.train_labels
// mnist.test_images
// mnist.test_labels
let nn;
let trainrun = 1;
let train_index = 0;
let testrun = 1;
let test_index = 0;
let total_tests = 0;
let total_correct = 0;
// images in LHS:
let doodle, demo;
let doodle_exists = false;
let demo_exists = false;
let mousedrag = false; // are we in the middle of a mouse drag drawing?
// save inputs to global var to inspect
// type these names in console
var train_inputs, test_inputs, demo_inputs, doodle_inputs;
//AB.runloggedin = true;
// Matrix.randomize() is changed to point to this. Must be defined by user of Matrix.
function randomWeight()
{
return ( AB.randomFloatAtoB ( -0.5, 0.5 ) );
// Coding Train default is -1 to 1
}
// CSS trick
// make run header bigger
$("#runheaderbox").css ( { "max-height": "95vh" } );
//--- start of AB.msgs structure: ---------------------------------------------------------
// We output a serious of AB.msgs to put data at various places in the run header
var thehtml;
// 1 Doodle header
thehtml = "<hr> <h1> 1. Doodle </h1> Top row: Doodle (left) and shrunk (right). <br> " +
" Draw your doodle in top LHS. <button onclick='wipeDoodle();' class='normbutton' >Clear doodle</button> <br> ";
AB.msg ( thehtml, 1 );
// 2 Doodle variable data (guess)
// 3 Training header
thehtml = "<hr> <h1> 2. Training </h1> Middle row: Training image magnified (left) and original (right). <br> " +
" <button onclick='do_training = false;' class='normbutton' >Stop training</button> <br> ";
AB.msg ( thehtml, 3 );
// 4 variable training data
// 5 Testing header
thehtml = "<h3> Hidden tests </h3> " ;
AB.msg ( thehtml, 5 );
// 6 variable testing data
// 7 Demo header
thehtml = "<hr> <h1> 3. Demo </h1> Bottom row: Test image magnified (left) and original (right). <br>" +
" The network is <i>not</i> trained on any of these images. <br> " +
" <button onclick='makeDemo();' class='normbutton' >Demo test image</button> <br> ";
AB.msg ( thehtml, 7 );
// 8 Demo variable data (random demo ID)
// 9 Demo variable data (changing guess)
const greenspan = "<span style='font-weight:bold; font-size:x-large; color:darkgreen'> " ;
//--- end of AB.msgs structure: ---------------------------------------------------------
function setup()
{
createCanvas ( canvaswidth, canvasheight );
doodle = createGraphics ( ZOOMPIXELS, ZOOMPIXELS ); // doodle on larger canvas
doodle.pixelDensity(1);
// JS load other JS
// maybe have a loading screen while loading the JS and the data set
AB.loadingScreen();
$.getScript ( "/uploads/codingtrain/matrix.js", function()
{
$.getScript ( "/uploads/codingtrain/nn.js", function()
{
$.getScript ( "/uploads/codingtrain/mnist.js", function()
{
console.log ("All JS loaded");
nn = new NeuralNetwork( noinput, nohidden, nooutput );
nn.setLearningRate ( learningrate );
loadData();
// saveData();
// AB.getAllData ( processAllData );
// if ( AB.onDesktop() )
// if ( AB.runloggedin )
// {
// // Definitely can save, not sure if can restore:
// AB.msg ( " <button onclick='saveData();' class='normbutton mybutton' >Save work</button> " );
// // Check if any data exists, if so make restore button
// AB.queryDataExists ( function ( exists ) // asynchronous - need callback function
// {
// if ( exists ) makeRestoreButton();
// });
// loadResources()
// }
});
});
});
}
// function removeSplash()
// {
// // touch/click on splash screen marks audio as good for JS to call without further human interaction
// //audio.play(); audio.pause();
// AB.removeSplash(); // remove splash screen
// splashClicked = true;
// AB.runReady = true; // start run loop
// }
// function makeSplash ( a )
// // replace splash screen with this HTML
// // show a "scoreboard" of all users who have saved data
// // the arg is the array returned by getAllData, an array of items ( userid, username, object )
// {
// var html = "<div style='max-width:600px; text-align:left;'>" +
// "<h1> MineCraft <img width=50 src='/uploads/starter/minecraft.1.jpg'> </h1> ";
// if ( AB.onDesktop() )
// {
// html = html + " <p> <b>Instructions:</b> Draw blocks using Arrow keys and PgUp, PgDn. </p> ";
// if ( AB.runloggedin )
// html = html + "<p> <b> Logged in: </b> " +
// // " <a href='https://ancientbrain.com/user.php?userid="+ AB.myuserid + "'>" + shortstring( AB.myusername ) + "</a>. " +
// " You are running " +
// " <a href='https://ancientbrain.com/docs.runs.php#runloggedin'>\"logged in\"</a>. " +
// " You can save your work to the server. </p>";
// else
// html = html + "<p style='background-color:#ffffcc;'> <b> Not logged in: </b> " +
// " You are not running " +
// " <a href='https://ancientbrain.com/docs.runs.php#runloggedin'>\"logged in\"</a>. " +
// " You cannot save your work to the server. To run logged in, log in and run this from the World page. </p> ";
// }
// else
// html = html + " <p> <b> Warning:</b> This World only works fully on desktop. </p> ";
// // scoreboard
// if ( a.length === 0 )
// {
// html = html + "<p> <b> Start: </b> Start MineCraft: <button style='vertical-align:text-bottom' id=splashbutton class=normbutton >Start</button> </p>" +
// " <p> No user has saved any creations yet. </p>" ;
// }
// else
// {
// html = html + "<p> <b> Start: </b> Start from scratch: <button style='vertical-align:text-bottom' id=splashbutton class=normbutton >Start</button> " +
// " Or load creation of previous user: </p>" +
// "<div class=horizontalscroll >" +
// "<table class=mytable style='background: rgba(238, 255, 255, 1.0);' >" +
// "<TR> <TD class=headertd> User </td> <TD class=headertd> Number of blocks </td><td class=headertd> Load creation </td></TR>";
// for ( var i = 0; i < a.length; i++ )
// {
// html = html + "<tr><td> <a href='https://ancientbrain.com/user.php?userid="+ a[i][0] + "'>" + shortstring( a[i][1] ) + "</a></td>" +
// "<td>" + a[i][2].length + "</td>" +
// "<td> <button onclick='loadCreation(" + i + ");' class=normbutton >Load</button> </td></tr>";
// // "Load" button i will call function to load object i (we have saved a list of all objects in memory)
// }
// html = html + "</table></div>";
// }
// return ( html + "</div>" );
// }
// function mysort (a,b)
// // how to compare two objects in the getAllData array
// // array of items ( userid, username, object )
// // sort by object length (no. of blocks)
// {
// var alen = a[2].length;
// var blen = b[2].length;
// if ( alen == blen ) return 0;
// if ( alen > blen ) return -1;
// if ( alen < blen ) return 1;
// }
// function loadCreation ( i )
// {
// removeSplash(); // now audio is ready
// drawFromArray ( allData[i][2] );
// }
// function loadResources() // asynchronous file loads - call initScene() when all finished
// {
// for ( var i = 0; i < FILE_ARRAY.length; i++ )
// startFileLoad ( i ); // launch n asynchronous file loads
// }
// function makeRestoreButton()
// {
// AB.msg ( " <button onclick='restoreData();' class='normbutton mybutton' >Restore work</button> ", 2 );
// }
// function saveData() // save BLOCKARRAY to server
// {
// // if no restore button exists, can make one now
// // if exists, this just overwrites it
// makeRestoreButton();
// // console.log ( "Saving " + BLOCKARRAY.length + " blocks to server" );
// AB.saveData ( nn );
// }
// function restoreData()
// {
// AB.restoreData ( function ( a )
// {
// // object returned from server is an array of blocks
// // console.log ( "Restoring " + a.length + " blocks from server" );
// drawFromArray (a);
// });
// }
// function processAllData ( a )
// // arg is the array returned by getAllData
// // makes a splash screen with "scoreboard"
// {
// AB.newSplash();
// // sort the array to get a sorted "human scoreboard"
// // the sort will be World specific
// a.sort ( mysort );
// // build splash contents from the array
// var html = makeSplash ( a );
// // replace splash contents
// $("#splash-inner").html( html );
// $("#splashbutton").click ( removeSplash );
// allData = a; // global var - save it for later
// }
// load data set from local file (on this server)
function loadData()
{
loadMNIST ( function(data)
{
mnist = data;
console.log ("All data loaded into mnist object:")
console.log(mnist);
AB.removeLoading(); // if no loading screen exists, this does nothing
});
}
function getImage ( img ) // make a P5 image object from a raw data array
{
let theimage = createImage (PIXELS, PIXELS); // make blank image, then populate it
theimage.loadPixels();
for (let i = 0; i < PIXELSSQUARED ; i++)
{
let bright = img[i];
let index = i * 4;
theimage.pixels[index + 0] = bright;
theimage.pixels[index + 1] = bright;
theimage.pixels[index + 2] = bright;
theimage.pixels[index + 3] = 255;
}
theimage.updatePixels();
return theimage;
}
function getInputs ( img ) // convert img array into normalised input array
{
let inputs = [];
for (let i = 0; i < PIXELSSQUARED ; i++)
{
let bright = img[i];
inputs[i] = bright / 255; // normalise to 0 to 1
}
return ( inputs );
}
function trainit (show) // train the network with a single exemplar, from global var "train_index", show visual on or off
{
let img = mnist.train_images[train_index];
let label = mnist.train_labels[train_index];
// optional - show visual of the image
if (show)
{
var theimage = getImage ( img ); // get image from data array
image ( theimage, 0, ZOOMPIXELS+50, ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( theimage, ZOOMPIXELS+50, ZOOMPIXELS+50, PIXELS, PIXELS ); // original
}
// set up the inputs
let inputs = getInputs ( img ); // get inputs from data array
// set up the outputs
let targets = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
targets[label] = 1; // change one output location to 1, the rest stay at 0
// console.log(train_index);
//console.log(inputs);
// console.log(targets);
train_inputs = inputs; // can inspect in console
nn.train ( inputs, targets );
thehtml = " trainrun: " + trainrun + "<br> no: " + train_index ;
AB.msg ( thehtml, 4 );
train_index++;
if ( train_index == NOTRAIN )
{
train_index = 0;
console.log( "finished trainrun: " + trainrun );
trainrun++;
}
}
function testit() // test the network with a single exemplar, from global var "test_index"
{
let img = mnist.test_images[test_index];
let label = mnist.test_labels[test_index];
// set up the inputs
let inputs = getInputs ( img );
test_inputs = inputs; // can inspect in console
let prediction = nn.predict(inputs); // array of outputs
let guess = findMax(prediction); // the top output
total_tests++;
if (guess == label) total_correct++;
let percent = (total_correct / total_tests) * 100 ;
thehtml = " testrun: " + testrun + "<br> no: " + total_tests + " <br> " +
" correct: " + total_correct + "<br>" +
" score: " + greenspan + percent.toFixed(2) + "</span>";
AB.msg ( thehtml, 6 );
test_index++;
if ( test_index == NOTEST )
{
console.log( "finished testrun: " + testrun + " score: " + percent.toFixed(2) );
testrun++;
test_index = 0;
total_tests = 0;
total_correct = 0;
}
}
//--- find no.1 (and maybe no.2) output nodes ---------------------------------------
// (restriction) assumes array values start at 0 (which is true for output nodes)
function find12 (a) // return array showing indexes of no.1 and no.2 values in array
{
let no1 = 0;
let no2 = 0;
let no1value = 0;
let no2value = 0;
for (let i = 0; i < a.length; i++)
{
if (a[i] > no1value)
{
no1 = i;
no1value = a[i];
}
else if (a[i] > no2value)
{
no2 = i;
no2value = a[i];
}
}
var b = [ no1, no2 ];
return b;
}
// just get the maximum - separate function for speed - done many times
// find our guess - the max of the output nodes array
function findMax (a)
{
let no1 = 0;
let no1value = 0;
for (let i = 0; i < a.length; i++)
{
if (a[i] > no1value)
{
no1 = i;
no1value = a[i];
}
}
return no1;
}
// --- the draw function -------------------------------------------------------------
// every step:
function draw()
{
// check if libraries and data loaded yet:
if ( typeof mnist == 'undefined' ) return;
// how can we get white doodle on black background on yellow canvas?
// background('#ffffcc'); doodle.background('black');
background ('yellow');
if ( do_training )
{
// do some training per step
for (let i = 0; i < TRAINPERSTEP; i++)
{
if (i == 0) trainit(true); // show only one per step - still flashes by
else trainit(false);
}
// do some testing per step
for (let i = 0; i < TESTPERSTEP; i++)
testit();
}
// keep drawing demo and doodle images
// and keep guessing - we will update our guess as time goes on
if ( demo_exists )
{
drawDemo();
guessDemo();
}
if ( doodle_exists )
{
drawDoodle();
guessDoodle();
}
// detect doodle drawing
// (restriction) the following assumes doodle starts at 0,0
if ( mouseIsPressed ) // gets called when we click buttons, as well as if in doodle corner
{
// console.log ( mouseX + " " + mouseY + " " + pmouseX + " " + pmouseY );
var MAX = ZOOMPIXELS + 20; // can draw up to this pixels in corner
if ( (mouseX < MAX) && (mouseY < MAX) && (pmouseX < MAX) && (pmouseY < MAX) )
{
mousedrag = true; // start a mouse drag
doodle_exists = true;
doodle.stroke('white');
doodle.strokeWeight( DOODLE_THICK );
doodle.line(mouseX, mouseY, pmouseX, pmouseY);
}
}
else
{
// are we exiting a drawing
if ( mousedrag )
{
mousedrag = false;
// console.log ("Exiting draw. Now blurring.");
doodle.filter (BLUR, DOODLE_BLUR); // just blur once
console.log (doodle);
}
}
}
//--- demo -------------------------------------------------------------
// demo some test image and predict it
// get it from test set so have not used it in training
function makeDemo()
{
demo_exists = true;
var i = AB.randomIntAtoB ( 0, NOTEST - 1 );
demo = mnist.test_images[i];
var label = mnist.test_labels[i];
thehtml = "Test image no: " + i + "<br>" +
"Classification: " + label + "<br>" ;
AB.msg ( thehtml, 8 );
// type "demo" in console to see raw data
}
function drawDemo()
{
var theimage = getImage ( demo );
// console.log (theimage);
image ( theimage, 0, canvasheight - ZOOMPIXELS, ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( theimage, ZOOMPIXELS+50, canvasheight - ZOOMPIXELS, PIXELS, PIXELS ); // original
}
function guessDemo()
{
let inputs = getInputs ( demo );
demo_inputs = inputs; // can inspect in console
let prediction = nn.predict(inputs); // array of outputs
let guess = findMax(prediction); // the top output
thehtml = " We classify it as: " + greenspan + guess + "</span>" ;
AB.msg ( thehtml, 9 );
}
//--- doodle -------------------------------------------------------------
function drawDoodle()
{
// doodle is createGraphics not createImage
let theimage = doodle.get();
//console.log (theimage);
// theimage.resize ( PIXELS, PIXELS );
// console.log (theimage);
// theimage.loadPixels();
// console.log (theimage);
image ( theimage, 0, 0, ZOOMPIXELS, ZOOMPIXELS ); // original
//image ( theimage, ZOOMPIXELS+50, 0, PIXELS, PIXELS ); // shrunk
}
function guessDoodle()
{
// doodle is createGraphics not createImage
let img = doodle.get();
img.resize ( PIXELS, PIXELS );
img.loadPixels();
// set up inputs
let inputs = [];
for (let i = 0; i < PIXELSSQUARED ; i++)
{
inputs[i] = img.pixels[i * 4] / 255;
}
doodle_inputs = inputs;
// can inspect in console
//console.log("akshara")
//console.log(doodle_inputs);
for (let j =0; j<inputs.length;j++){
if(inputs[j]>0.6){
inputs[j] = 0.999;
}
if(inputs[j]<0.3){
inputs[j] = 0.0;
}
}
// feed forward to make prediction
let prediction = nn.predict(inputs); // array of outputs
let b = find12(prediction); // get no.1 and no.2 guesses
thehtml = " We classify it as: " + greenspan + b[0] + "</span> <br>" +
" No.2 guess is: " + greenspan + b[1] + "</span>";
AB.msg ( thehtml, 2 );
}
function wipeDoodle()
{
doodle_exists = false;
doodle.background('black');
}
// --- debugging --------------------------------------------------
// in console
// showInputs(demo_inputs);
// showInputs(doodle_inputs);
function showInputs ( inputs )
// display inputs row by row, corresponding to square of pixels
{
var str = "";
for (let i = 0; i < inputs.length; i++)
{
if ( i % PIXELS == 0 ) str = str + "\n"; // new line for each row of pixels
var value = inputs[i];
str = str + " " + value.toFixed(2) ;
}
console.log (str);
}